BiTraP: Bi-Directional Pedestrian Trajectory Prediction With Multi-Modal Goal Estimation

Yao, Yu; Atkins, Ella M.; Johnson-Roberson, Matthew; Vasudevan, Ram; Du, Xiaomeng

doi:10.1109/lra.2021.3056339

Cited by 106 publications

(84 citation statements)

References 32 publications

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“…Bi-RNNs [9] address a drawback of Recurrent Neural Networks (RNNs), which is that they cannot take the future into account when they encode an input, which may be desirable [10] for some cases. For example, in the case of pedestrian trajectory prediction [11], [12], one could expect that some movements are influenced by anticipation of a potential obstacle. Bi-RNNs produce two outputs, one that is obtained by reading the input forward and one by reading the input backwards.…”

Section: B From Bi-rnns To U-rnnsmentioning

confidence: 99%

Asymmetrical Bi-RNN for pedestrian trajectory encoding

Rozenberg,

Gesnouin,

Moutarde

2021

Preprint

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Pedestrian motion behavior involves a combination of individual goals and social interactions with other agents. In this article, we present an asymmetrical bidirectional recurrent neural network architecture called U-RNN to encode pedestrian trajectories and evaluate its relevance to replace LSTMs for various forecasting models. Experimental results on the Trajnet++ benchmark show that the U-LSTM variant yields better results regarding every available metrics (ADE, FDE, Collision rate) than common trajectory encoders for a variety of approaches and interaction modules, suggesting that the proposed approach is a viable alternative to the de facto sequence encoding RNNs. Our implementation of the asymmetrical Bi-RNNs for the Trajnet++ benchmark is available at: github.com/JosephGesnouin/Asymmetrical-Bi-RNNs-toencode-pedestrian-trajectories

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Section: B From Bi-rnns To U-rnnsmentioning

confidence: 99%

Asymmetrical Bi-RNN for pedestrian trajectory encoding

Rozenberg,

Gesnouin,

Moutarde

2021

Preprint

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“…TNT [50] decomposes the prediction task into three stages: predicting potential target states, generating trajectory state sequences, and estimating trajectory likelihoods. BiTraP [43] uses a bi-directional decoder on the predicted goal to improve long-term trajectory prediction.…”

Section: Related Workmentioning

confidence: 99%

“…However, predicting another agent's future actions is challenging because it depends on numerous factors including the environment and the agent's internal state (e.g., its intentions and goals). Recent work [12,28,32,43,48,50] has explored goal-driven methods for future trajectory prediction, which explicitly estimate the goal state (e.g., destination) of an object to help Although these recent models make an important step forward, they make the simplistic assumption that agents' trajectories are based only on a long-term goal that they are working towards. However, work in psychology and cognitive science shows that people base their actions not on a single long-term goal, but instead on series of goals at different time scales [10,14,37,38].…”

Section: Introductionmentioning

confidence: 99%

Stepwise Goal-Driven Networks for Trajectory Prediction

Wang,

et al. 2021

Preprint

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We propose to predict the future trajectories of observed agents (e.g., pedestrians or vehicles) by estimating and using their goals at multiple time scales. We argue that the goal of a moving agent may change over time, and modeling goals continuously provides more accurate and detailed information for future trajectory estimation. In this paper, we present a novel recurrent network for trajectory prediction, called Stepwise Goal-Driven Network (SGNet). Unlike prior work that models only a single, long-term goal, SGNet estimates and uses goals at multiple temporal scales. In particular, the framework incorporates an encoder module that captures historical information, a stepwise goal estimator that predicts successive goals into the future, and a decoder module that predicts future trajectory. We evaluate our model on three first-person traffic datasets (HEV-I, JAAD, and PIE) as well as on two bird's eye view datasets (ETH and UCY), and show that our model outperforms the state-of-the-art methods in terms of both average and final displacement errors on all datasets. Code has been made available at: https://github.com/ ChuhuaW/SGNet.pytorch.

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“…ey find the most likely future trajectory from the recommended trajectories and then refine them to ensure the diversity of the final prediction. Moreover, Yao et al proposed a bidirectional multimodal trajectory prediction method (BiTrap) based on target estimation [12].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

Cao

Zhao

Qiaoqiong

et al. 2021

Journal of Advanced Transportation

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Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction system is developed to collect vehicle trajectories and their left-turn, go straight, and right-turn labels to train turning recognition models and multilayer LSTM deep neural networks for the prediction task. Then, when performing vehicle trajectory prediction, we propose the vehicle heading angle change trend method to recognize the future move of the target vehicle to turn left, go straight, and turn right based on the trajectory data characteristics of the target vehicle before passing the stop line. Finally, we use the trained multilayer LSTM models of turning left, going straight, and turning right to predict the trajectory of the target vehicle through the intersection. Based on the TensorFlow-GPU platform, we use Yolov5-DeepSort to automatically extract vehicle trajectory data at unsignalized intersections. The experimental results show that the proposed method performs well and has a good performance in both speed and accuracy evaluation.

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BiTraP: Bi-Directional Pedestrian Trajectory Prediction With Multi-Modal Goal Estimation

Cited by 106 publications

References 32 publications

Asymmetrical Bi-RNN for pedestrian trajectory encoding

Asymmetrical Bi-RNN for pedestrian trajectory encoding

Stepwise Goal-Driven Networks for Trajectory Prediction

Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

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